Location-Based Recommender System for Mobile Devices on University Campus

2015 ◽  
Keyword(s):  
Mobile Devices ◽  
Recommender System ◽  
University Campus

GimmeHop: A recommender system for mobile devices using ontology reasoners and fuzzy logic

2020 ◽  
Vol 401 ◽  
pp. 55-77 ◽  
Author(s):  
Ignacio Huitzil ◽  
Fernando Alegre ◽  
Fernando Bobillo
Keyword(s):  
Fuzzy Logic ◽  
Mobile Devices ◽  
Recommender System

Context-Aware Recommender System for Location-Based Advertising

2011 ◽  
Vol 467-469 ◽  
pp. 2091-2096 ◽  
Author(s):  
Hyun Chul Ahn ◽  
Kyoung Jae Kim
Keyword(s):  
Mobile Devices ◽  
Recommender System ◽  
Service Industries ◽  
Classification Rule ◽  
Mobile Service ◽  
Context Aware ◽  
Good Market ◽  
Proposed Model ◽  
Market Opportunities ◽  
Marketing Tool

Demand for context-aware systems continues to grow due to the diffusion of mobile devices. This trend may represent good market opportunities for mobile service industries. Thus, context-aware or location-based advertising (LBA) has been an interesting marketing tool for many companies. However, some studies reported that the performance of context-aware marketing or advertising has been quite disappointing. In this study, we propose a novel context-aware recommender system for LBA. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user’s needs type. In particular, we employ a classification rule to understand user’s needs type using a decision tree algorithm. We empirically validated the effectiveness of the proposed model by using a real-world dataset. Experimental results show that our model makes more accurate and satisfactory advertisements than comparative systems.

scholarly journals Towards MANET-based Recommender Systems for Open Facilities

2021 ◽  
Author(s):  
Jürgen Dunkel ◽  
Ramón Hermoso
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Mobile Devices ◽  
Recommender System ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Metrics ◽  
Sensor Data ◽  
Mobile Ad Hoc

AbstractNowadays, most recommender systems are based on a centralized architecture, which can cause crucial issues in terms of trust, privacy, dependability, and costs. In this paper, we propose a decentralized and distributed MANET-based (Mobile Ad-hoc NETwork) recommender system for open facilities. The system is based on mobile devices that collect sensor data about users locations to derive implicit ratings that are used for collaborative filtering recommendations. The mechanisms of deriving ratings and propagating them in a MANET network are discussed in detail. Finally, extensive experiments demonstrate the suitability of the approach in terms of different performance metrics.

Interactive Personalized Catalogue for M-Commerce

2008 ◽  
pp. 218-228
Author(s):  
Sheng-Uei Guan ◽  
Yuan Sherng Tay
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Recommender System ◽  
User Interfaces ◽  
Personal Digital Assistant ◽  
User Preference ◽  
Screen Size ◽  
Size Constraints ◽  
Small Form Factor ◽  
Small Screen

M-commerce possesses two distinctive characteristics that distinguish it from traditional e-commerce: the mobile setting and the small form factor of mobile devices. Of these, the size of a mobile device will remain largely unchanged due to the tradeoff between size and portability. Small screen size and limited input capabilities pose a great challenge for developers to conceptualize user interfaces that have good usability while working within the size constraints of the device. In response to the limited screen size of mobile devices, there has been unspoken consensus that certain tools must be made available to aid users in coping with the relatively large volume of information. Recommender systems have been proposed to narrow down choices before presenting them to the user (Feldman, 2000). We propose a product catalogue where browsing is directed by an integrated recommender system. The recommender system is to take incremental feedback in return for browsing assistance. Product appearance in the catalogue will be dynamically determined at runtime based on user preference detected by the recommender system. The design of our hybrid m-commerce catalogue recommender system investigated the typical constraints of m-commerce applications to conceptualize a suitable catalogue interface. The scope was restricted to the case of having personal digital assistant (PDA) as the mobile device. Thereafter, a preference detection technique was developed to serve as the recommender layer of the system.

Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

2015 ◽  
pp. 2159-2178
Author(s):  
Sílvio César Cazella ◽  
Jorge Luiz Victória Barbosa ◽  
Eliseo Berni Reategui ◽  
Patricia Alejandra Behar ◽  
Otavio Costa Acosta
Keyword(s):  
Learning Environment ◽  
Recommender Systems ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Recommender System ◽  
Information Filtering ◽  
Likert Scale ◽  
Learning Objects ◽  
Mobile Environments ◽  
Proposed Model

Mobile learning is about increasing learners' capability to carry their own learning environment along with them. Recommender Systems are widely used nowadays, especially in e-commerce sites and mobile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifically for the recommendation of learning objects in mobile devices. The advantage of using Recommender Systems in mobile devices is that it is an easy way to deliver recommendations to students. Based on this scenario, this chapter presents a model of a recommender system based on information filtering for mobile environments. The proposed model was implemented in a prototype aimed to recommend learning objects in mobile devices. The evaluation of the received recommendations was conducted using a Likert scale of 5 points. At the end of this chapter, some future works are described.

Understanding User-Curated Playlists on Spotify

2017 ◽  
Vol 8 (4) ◽  
pp. 44-59 ◽  
Author(s):  
Martin Pichl ◽  
Eva Zangerle ◽  
Günther Specht
Keyword(s):  
Recommender Systems ◽  
Mobile Devices ◽  
Recommender System ◽  
Spectral Clustering ◽  
Audio Features ◽  
Current Context ◽  
Clustering Approach ◽  
Music Streaming ◽  
Filtering Techniques

Music streaming platforms enable people to access millions of tracks using computers and mobile devices. However, users cannot browse manually millions of tracks to find music they like. Building recommender systems suggesting music fitting the current context of a user is a challenging task. A deeper understanding for the characteristics of user-curated playlists naturally contributes to more personalized recommendations. To get a deeper understanding of how users organize music nowadays, we analyze user-curated playlists from the music streaming platform Spotify. Based on the audio features of the tracks, we find an explanation of differences in the playlists using a PCA and are able to group playlists using spectral clustering. Our findings about playlist characteristics can be exploited in a SVD-based music recommender system and our proposed clustering approach for finding groups of similar playlists is easy to integrate into a recommender system using pre- or post-filtering techniques.

scholarly journals Improving Children’s Experience on a Mobile EdTech Platform through a Recommender System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Almudena Ruiz-Iniesta ◽  
Luis Melgar ◽  
Alejandro Baldominos ◽  
David Quintana
Keyword(s):  
Collaborative Filtering ◽  
Mobile Devices ◽  
Recommender System ◽  
Experimental Results ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Smile and Learn is an EdTech digital publisher that offers a smart library of close to 100 educational stories and gaming apps for mobile devices aimed at children aged 2 to 10 and their families. Given the complexity of navigating the content, a recommender system was developed. The system consists of two major components: one that generates content recommendations and another that provides explanations and recommendations relevant to parents and educators. The former was implemented as a hybrid recommender system that combines three kinds of recommendations. Among these, we introduce a collaborative filtering adapted to overcome specific limitations associated with younger users. The approach described in this work was tested on real users of the platform. The experimental results suggest that this recommendation model is suitable to suggest apps to children and increase their engagement in terms of usage time and number of games played.

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